{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "exec(open('Step_2_ForeignExposure_Public.py').read())\n",
    "# import ray\n",
    "# ray.shutdown()\n",
    "# ray.init()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the Data\n",
    "HH = pickle.load(open('../Data/CFM_GQ/CFM_Cleaned.p','rb'))['HH']\n",
    "CFM = pickle.load(open('../Data/CFM_GQ/AssetPanel_CFM_GQ_M.p','rb'))\n",
    "\n",
    "CFM = CFM.join(HH['Weight']).reset_index()\n",
    "CFM['QDate'] = CFM['period'].map(lambda x: datetime.datetime(x.year,3*(x.quarter-1)+1,1))\n",
    "CFM['Obs'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the Detailed CFM Panel Data\n",
    "CFM_Details = pickle.load(open('../Data/CFM_GQ/AssetPanel_CFM_GQ_Details.p','rb'))\n",
    "\n",
    "CFM_MatchInfo = CFM_Details.set_index(['period','hldid','FI'])['Asset_Total'].unstack()\n",
    "CFM_MatchInfo['Total'] = CFM_MatchInfo.sum(axis=1)\n",
    "CFM_MatchInfo['Matched'] = CFM_MatchInfo['Total'] - CFM_MatchInfo['Not Matched']\n",
    "CFM_MatchInfo = CFM_MatchInfo[['Total','Matched','Not Matched']]\n",
    "CFM_MatchInfo['MatchFlag'] = (CFM_MatchInfo['Matched']>0)*1\n",
    "CFM_MatchInfo = CFM_MatchInfo.reset_index().merge(right=HH.reset_index()[['period','hldid','Weight']],how='left',on=['period','hldid'])\n",
    "CFM_MatchInfo['MatchTotal'] = CFM_MatchInfo['Total']*CFM_MatchInfo['MatchFlag']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Statistics: Setup\n",
    "vv = 3\n",
    "ExposureVar = 'Exposure_'+str(vv)\n",
    "IdxTime = 'period'\n",
    "DateList = CFM[IdxTime].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Quantiles\n",
    "def TempFun_WeightedQuantile(DS,Var_V,Var_W,q=[0.25,0.5,0.75]):\n",
    "    TempDS = DS.loc[:,[Var_V,Var_W]].dropna().sort_values(by=Var_V)\n",
    "    TempDS[Var_W] = TempDS[Var_W]/TempDS[Var_W].sum()\n",
    "    TempDS[Var_W] = TempDS[Var_W].cumsum()\n",
    "    return interp1d(TempDS[Var_W], TempDS[Var_V],bounds_error=False,fill_value=(TempDS[Var_V].min(),TempDS[Var_V].max()))(q)\n",
    "\n",
    "QuantList = [0.25,0.50,0.75]\n",
    "\n",
    "TempResults = [TempFun_WeightedQuantile(CFM.loc[CFM[IdxTime]==TempDate,:], ExposureVar, 'Weight', QuantList) \\\n",
    "               for TempDate in DateList]\n",
    "\n",
    "Stat_Quant = pd.DataFrame(TempResults,index=DateList,columns=QuantList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Mean\n",
    "def TempFun_WeightedAverage(DS,Var_V,Var_W):\n",
    "    Var_W = Var_W if isinstance(Var_W,list) else [Var_W]\n",
    "    TempVarList = [Var_V]+Var_W \n",
    "    TempDS = DS.loc[:,TempVarList].dropna().sort_values(by=Var_V)\n",
    "    TempDS['W'] = TempDS[Var_W].prod(axis=1)\n",
    "    return (TempDS[Var_V]*TempDS['W']).sum()/TempDS['W'].sum()\n",
    "\n",
    "TempResults = [TempFun_WeightedAverage(CFM.loc[CFM[IdxTime]==TempDate,:], ExposureVar, 'Weight') \\\n",
    "               for TempDate in DateList]\n",
    "\n",
    "Stat_Avg = pd.DataFrame(TempResults,index=DateList,columns=['Avg'])\n",
    "\n",
    "TempResults = [TempFun_WeightedAverage(CFM.loc[CFM[IdxTime]==TempDate,:], ExposureVar, ['Asset_Total','Weight']) \\\n",
    "               for TempDate in DateList]\n",
    "\n",
    "Stat_WAvg = pd.DataFrame(TempResults,index=DateList,columns=['WAvg'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "SumStat_TS = pd.concat([Stat_Quant,Stat_Avg,Stat_WAvg],axis=1).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Histogram\n",
    "Hist_Gap = 0.05\n",
    "TempBins = [-np.inf]+list(np.linspace(0,1,num=int(1/Hist_Gap)+1))+[np.inf]\n",
    "TempLabels = TempBins[1:-1]+[1+Hist_Gap]\n",
    "CFM['ExposureGroup'] = pd.cut(CFM[ExposureVar],bins=TempBins,labels=TempLabels)\n",
    "\n",
    "def TempFun_WeightedHistogram(DS,Var_V,Var_W,Var_G):\n",
    "    Var_W = Var_W if isinstance(Var_W,list) else [Var_W]\n",
    "    TempVarList = [Var_V,Var_G]+Var_W \n",
    "    TempDS = DS.loc[:,TempVarList].dropna()\n",
    "    \n",
    "    TempDS['W'] = TempDS[Var_W].prod(axis=1)\n",
    "    TempDS['W'] = TempDS['W']/TempDS['W'].sum()\n",
    "    TempDS['VW'] = TempDS[Var_V]*TempDS['W']\n",
    "\n",
    "    Hist = TempDS.groupby(Var_G)['VW'].sum()\n",
    "    Hist = Hist/Hist.sum()\n",
    "\n",
    "    return Hist\n",
    "\n",
    "TempResults = [TempFun_WeightedHistogram(CFM.loc[CFM[IdxTime]==TempDate,:], 'Obs', 'Weight', 'ExposureGroup') \\\n",
    "               for TempDate in DateList]\n",
    "\n",
    "Hist_Obs = pd.DataFrame(TempResults,index=DateList).mean()\n",
    "\n",
    "TempResults = [TempFun_WeightedHistogram(CFM.loc[CFM[IdxTime]==TempDate,:], 'Asset_Total', 'Weight', 'ExposureGroup') \\\n",
    "               for TempDate in DateList]\n",
    "\n",
    "Hist_Asset = pd.DataFrame(TempResults,index=DateList).mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "Hist_Avg = pd.concat([Hist_Obs,Hist_Asset],axis=1,keys=['Obs','Asset']).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Conditional Statistics of Integrated Households\n",
    "TempDS = CFM.copy()\n",
    "TempDict = {}\n",
    "for cc in np.linspace(0,0.5,num=51):\n",
    "    TempInd = TempDS[ExposureVar]>cc\n",
    "    TempDS['Obs_Int'] = 0\n",
    "    TempDS.loc[TempInd, 'Obs_Int'] = 1\n",
    "    TempDS['Asset_Int'] = 0\n",
    "    TempDS.loc[TempInd,'Asset_Int'] = TempDS.loc[TempInd,'Asset_Total']\n",
    "\n",
    "    TempFun = lambda TempDate: TempFun_WeightedQuantile(TempDS.loc[(TempDS[IdxTime]==TempDate) & TempInd,:], ExposureVar, 'Weight', QuantList)\n",
    "    Temp_Quantile = pd.DataFrame([TempFun(TempDate) for TempDate in DateList],index=DateList,columns=QuantList)\n",
    "\n",
    "    TempFun = lambda TempDate: TempFun_WeightedAverage(TempDS.loc[(TempDS[IdxTime]==TempDate) & TempInd,:], ExposureVar, 'Weight') \n",
    "    Temp_Avg = pd.DataFrame([TempFun(TempDate) for TempDate in DateList],index=DateList,columns=['Avg'])\n",
    "\n",
    "    TempFun = lambda TempDate: TempFun_WeightedAverage(TempDS.loc[(TempDS[IdxTime]==TempDate),:], 'Obs_Int', 'Weight') \n",
    "    Temp_Obs = pd.DataFrame([TempFun(TempDate) for TempDate in DateList],index=DateList,columns=['Obs'])\n",
    "\n",
    "    TempFun = lambda TempDate: TempFun_WeightedAverage(TempDS.loc[(TempDS[IdxTime]==TempDate),:], 'Asset_Int', 'Weight') \n",
    "    Temp_Asset_Int = pd.Series([TempFun(TempDate) for TempDate in DateList],index=DateList)\n",
    "\n",
    "    TempFun = lambda TempDate: TempFun_WeightedAverage(TempDS.loc[(TempDS[IdxTime]==TempDate),:], 'Asset_Total', 'Weight') \n",
    "    Temp_Asset_Total = pd.Series([TempFun(TempDate) for TempDate in DateList],index=DateList)\n",
    "\n",
    "    TempDict[cc] = pd.concat([Temp_Obs,Temp_Quantile,Temp_Avg,(Temp_Asset_Int/Temp_Asset_Total).rename('Asset')],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "Stat_IntHH_TS = pd.concat(TempDict,axis=1).swaplevel(axis=1).sort_index(axis=1)\n",
    "Stat_IntHH_Avg = Stat_IntHH_TS.mean().unstack(level=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "## GQ Return Coverage\n",
    "TempResults = [TempFun_WeightedAverage(CFM_MatchInfo.loc[CFM_MatchInfo[IdxTime]==TempDate,:], 'MatchFlag', 'Weight') \\\n",
    "               for TempDate in DateList]\n",
    "\n",
    "MatchStat_AvgObs = pd.DataFrame(TempResults,index=DateList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "TempResults_1 = [TempFun_WeightedAverage(CFM_MatchInfo.loc[CFM_MatchInfo[IdxTime]==TempDate,:], 'MatchTotal', 'Weight') \\\n",
    "                 for TempDate in DateList]\n",
    "TempResults_2 = [TempFun_WeightedAverage(CFM_MatchInfo.loc[CFM_MatchInfo[IdxTime]==TempDate,:], 'Total', 'Weight') \\\n",
    "                 for TempDate in DateList]\n",
    "\n",
    "MatchStat_AvgAsset = pd.DataFrame(TempResults_1,index=DateList)/pd.DataFrame(TempResults_2,index=DateList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average coverage for HH observation is  89.82 %\n",
      "The average coverage for HH asset is  95.41 %\n"
     ]
    }
   ],
   "source": [
    "print('The average coverage for HH observation is ', np.round(MatchStat_AvgObs[0].mean()*100,2), '%')\n",
    "print('The average coverage for HH asset is ', np.round(MatchStat_AvgAsset[0].mean()*100,2),\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>period</th>\n",
       "      <th>hldid</th>\n",
       "      <th>Total</th>\n",
       "      <th>Matched</th>\n",
       "      <th>Not Matched</th>\n",
       "      <th>MatchFlag</th>\n",
       "      <th>Weight</th>\n",
       "      <th>MatchTotal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-10-01</td>\n",
       "      <td>1342</td>\n",
       "      <td>88800.0</td>\n",
       "      <td>88800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2189</td>\n",
       "      <td>88800.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014-10-01</td>\n",
       "      <td>2629</td>\n",
       "      <td>151084.0</td>\n",
       "      <td>151084.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2546</td>\n",
       "      <td>151084.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014-10-01</td>\n",
       "      <td>3215</td>\n",
       "      <td>101250.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>101250.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5089</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-10-01</td>\n",
       "      <td>3891</td>\n",
       "      <td>156887.0</td>\n",
       "      <td>120940.0</td>\n",
       "      <td>35947.0</td>\n",
       "      <td>1</td>\n",
       "      <td>14087</td>\n",
       "      <td>156887.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-10-01</td>\n",
       "      <td>5319</td>\n",
       "      <td>17500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17500.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2089</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <th>45278</th>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>683425</td>\n",
       "      <td>96500.0</td>\n",
       "      <td>14000.0</td>\n",
       "      <td>82500.0</td>\n",
       "      <td>1</td>\n",
       "      <td>19970</td>\n",
       "      <td>96500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45279</th>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>683439</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2518</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45280</th>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>683451</td>\n",
       "      <td>66100.0</td>\n",
       "      <td>66100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>22602</td>\n",
       "      <td>66100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45281</th>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>683457</td>\n",
       "      <td>267073.0</td>\n",
       "      <td>267073.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>6273</td>\n",
       "      <td>267073.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45282</th>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>683489</td>\n",
       "      <td>26250.0</td>\n",
       "      <td>26250.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>21234</td>\n",
       "      <td>26250.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45283 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          period   hldid     Total   Matched  Not Matched  MatchFlag  Weight  \\\n",
       "0     2014-10-01    1342   88800.0   88800.0          0.0          1    2189   \n",
       "1     2014-10-01    2629  151084.0  151084.0          0.0          1    2546   \n",
       "2     2014-10-01    3215  101250.0       0.0     101250.0          0    5089   \n",
       "3     2014-10-01    3891  156887.0  120940.0      35947.0          1   14087   \n",
       "4     2014-10-01    5319   17500.0       0.0      17500.0          0    2089   \n",
       "...          ...     ...       ...       ...          ...        ...     ...   \n",
       "45278 2018-12-01  683425   96500.0   14000.0      82500.0          1   19970   \n",
       "45279 2018-12-01  683439    1800.0    1500.0        300.0          1    2518   \n",
       "45280 2018-12-01  683451   66100.0   66100.0          0.0          1   22602   \n",
       "45281 2018-12-01  683457  267073.0  267073.0          0.0          1    6273   \n",
       "45282 2018-12-01  683489   26250.0   26250.0          0.0          1   21234   \n",
       "\n",
       "       MatchTotal  \n",
       "0         88800.0  \n",
       "1        151084.0  \n",
       "2             0.0  \n",
       "3        156887.0  \n",
       "4             0.0  \n",
       "...           ...  \n",
       "45278     96500.0  \n",
       "45279      1800.0  \n",
       "45280     66100.0  \n",
       "45281    267073.0  \n",
       "45282     26250.0  \n",
       "\n",
       "[45283 rows x 8 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CFM_MatchInfo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Asset_Foreign_2</th>\n",
       "      <th>Asset_Foreign_3</th>\n",
       "      <th>Exposure_1</th>\n",
       "      <th>Exposure_2</th>\n",
       "      <th>Exposure_3</th>\n",
       "      <th>Weight</th>\n",
       "      <th>QDate</th>\n",
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       "      <th>ExposureGroup</th>\n",
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       "      <td>0.0</td>\n",
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       "      <td>5089</td>\n",
       "      <td>2014-10-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014-10-01</td>\n",
       "      <td>3891</td>\n",
       "      <td>156887.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18405.174692</td>\n",
       "      <td>18405.174692</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.117315</td>\n",
       "      <td>0.117315</td>\n",
       "      <td>14087</td>\n",
       "      <td>2014-10-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014-10-01</td>\n",
       "      <td>5319</td>\n",
       "      <td>17500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2089</td>\n",
       "      <td>2014-10-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45278</th>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>683425</td>\n",
       "      <td>96500.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.715021</td>\n",
       "      <td>8.715021</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>19970</td>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45279</th>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>683439</td>\n",
       "      <td>1800.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>361.514998</td>\n",
       "      <td>361.514998</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.200842</td>\n",
       "      <td>0.200842</td>\n",
       "      <td>2518</td>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45280</th>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>683451</td>\n",
       "      <td>66100.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>245.187763</td>\n",
       "      <td>245.187763</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.003709</td>\n",
       "      <td>0.003709</td>\n",
       "      <td>22602</td>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45281</th>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>683457</td>\n",
       "      <td>267073.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33729.296622</td>\n",
       "      <td>33729.296622</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.126292</td>\n",
       "      <td>0.126292</td>\n",
       "      <td>6273</td>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45282</th>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>683489</td>\n",
       "      <td>26250.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3311.789734</td>\n",
       "      <td>3311.789734</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.126163</td>\n",
       "      <td>0.126163</td>\n",
       "      <td>21234</td>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>45283 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          period   hldid  Asset_Total  Asset_Foreign_1  Asset_Foreign_2  \\\n",
       "0     2014-10-01    1342      88800.0              0.0      5624.052779   \n",
       "1     2014-10-01    2629     151084.0              0.0     13412.921680   \n",
       "2     2014-10-01    3215     101250.0              0.0         0.000000   \n",
       "3     2014-10-01    3891     156887.0              0.0     18405.174692   \n",
       "4     2014-10-01    5319      17500.0              0.0         0.000000   \n",
       "...          ...     ...          ...              ...              ...   \n",
       "45278 2018-12-01  683425      96500.0              0.0         8.715021   \n",
       "45279 2018-12-01  683439       1800.0              0.0       361.514998   \n",
       "45280 2018-12-01  683451      66100.0              0.0       245.187763   \n",
       "45281 2018-12-01  683457     267073.0              0.0     33729.296622   \n",
       "45282 2018-12-01  683489      26250.0              0.0      3311.789734   \n",
       "\n",
       "       Asset_Foreign_3  Exposure_1  Exposure_2  Exposure_3  Weight      QDate  \\\n",
       "0          5624.052779         0.0    0.063334    0.063334    2189 2014-10-01   \n",
       "1         13412.921680         0.0    0.088778    0.088778    2546 2014-10-01   \n",
       "2             0.000000         0.0    0.000000    0.000000    5089 2014-10-01   \n",
       "3         18405.174692         0.0    0.117315    0.117315   14087 2014-10-01   \n",
       "4             0.000000         0.0    0.000000    0.000000    2089 2014-10-01   \n",
       "...                ...         ...         ...         ...     ...        ...   \n",
       "45278         8.715021         0.0    0.000090    0.000090   19970 2018-10-01   \n",
       "45279       361.514998         0.0    0.200842    0.200842    2518 2018-10-01   \n",
       "45280       245.187763         0.0    0.003709    0.003709   22602 2018-10-01   \n",
       "45281     33729.296622         0.0    0.126292    0.126292    6273 2018-10-01   \n",
       "45282      3311.789734         0.0    0.126163    0.126163   21234 2018-10-01   \n",
       "\n",
       "       Obs ExposureGroup  \n",
       "0        1          0.10  \n",
       "1        1          0.10  \n",
       "2        1          0.00  \n",
       "3        1          0.15  \n",
       "4        1          0.00  \n",
       "...    ...           ...  \n",
       "45278    1          0.05  \n",
       "45279    1          0.25  \n",
       "45280    1          0.05  \n",
       "45281    1          0.15  \n",
       "45282    1          0.15  \n",
       "\n",
       "[45283 rows x 13 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CFM "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple statistics: how much of foreign assets are hold directly by households ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Within the integrated households:\n",
      "\n",
      "Only 29.0 % of them have foreign assets directly hold by them;\n",
      "Only 31.0 % of the foreign assets are directly hold by households.\n"
     ]
    }
   ],
   "source": [
    "TempInd = (CFM['Exposure_3']>0.1)\n",
    "TempInd_Direct = TempInd & (CFM['Exposure_1']>0)\n",
    "Freq_Direct = CFM.loc[TempInd_Direct, 'Weight'].sum()/ CFM.loc[TempInd, 'Weight'].sum()\n",
    "AssetPct_Direct = (CFM.loc[TempInd_Direct, 'Weight']*CFM.loc[TempInd_Direct, 'Asset_Foreign_1']).sum()/ (CFM.loc[TempInd, 'Weight']*CFM.loc[TempInd, 'Asset_Foreign_3']).sum()\n",
    "\n",
    "print(\"Within the integrated households:\\n\")\n",
    "print(\"Only\", np.round(Freq_Direct*100), \"% of them have foreign assets directly hold by them;\")\n",
    "print(\"Only\", np.round(AssetPct_Direct*100), \"% of the foreign assets are directly hold by households.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Figure\n",
    "Line_Main = MyGR.Line(MyGR.MyColor('Black'),'solid',3)\n",
    "Line_0 = MyGR.Line(MyGR.MyColor('Gray'),'dashed',2)\n",
    "Line_1 = MyGR.Line(MyGR.MyColor('Blue'),'dashed',1.5)\n",
    "Line_2 = MyGR.Line(MyGR.MyColor('Red'),'dashed',1.5)\n",
    "\n",
    "FigSize = (1/3,1/4)\n",
    "FontSize_1 = 6\n",
    "FontSize_2 = 4\n",
    "\n",
    "CutoffShare = 0.1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution over Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 550x412.5 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Distribution over Time\n",
    "Fig = MyGR.Setup_Fig(FigSize=FigSize)\n",
    "ax = Fig.add_subplot(1,1,1)\n",
    "### Mean\n",
    "Line_Main.Plot(SumStat_TS.index,SumStat_TS['Avg']*100,ax=ax,Label=\"Mean\")\n",
    "### Quantiles\n",
    "Line_0.Plot(SumStat_TS.index.date,SumStat_TS[0.5]*100,ax=ax,Label=\"Median\")\n",
    "Line_1.Plot(SumStat_TS.index.date,SumStat_TS[0.25]*100,ax=ax,Label=\"q25\")\n",
    "Line_2.Plot(SumStat_TS.index.date,SumStat_TS[0.75]*100,ax=ax,Label=\"q75\")\n",
    "### Setup the axis format\n",
    "MyGR.Setup_Ax(ax,XTickNbins=7,YTickNbins=7)\n",
    "ax.set_xlim(SumStat_TS.index.min(),SumStat_TS.index.max()) \n",
    "ax.set_xticks([datetime.datetime(yy,1,1) for yy in range(2015,2019)])\n",
    "ax.xaxis.set_major_formatter(matdates.DateFormatter(\"%y%b\"))\n",
    "plt.ylabel('Foreign Share of Assets (\\%)',fontsize=FontSize_1)\n",
    "plt.xlabel('Survey Date',fontsize=FontSize_1)\n",
    "ax.tick_params(labelsize=FontSize_1)\n",
    "plt.legend(fontsize=FontSize_2,loc='upper left',frameon=False)\n",
    "\n",
    "ax.hlines(CutoffShare*100,ax.get_xlim()[0],ax.get_xlim()[1], color=MyGR.MyColor('Black'),linestyles='dashed')\n",
    "\n",
    "plt.tight_layout()\n",
    "# ax.set_rasterized(True)\n",
    "plt.savefig('TableGraph/Exposure_HH_SumStatTS.eps', format='eps')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average summary statistics over time is\n",
      "0.25    0.004988\n",
      "0.5     0.077779\n",
      "0.75    0.135595\n",
      "Avg     0.094009\n",
      "WAvg    0.084893\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('The average summary statistics over time is')\n",
    "print(SumStat_TS.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Average Distribution across Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 550x412.5 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Histogram (Average Distribution)\n",
    "Fig = MyGR.Setup_Fig(FigSize=FigSize)\n",
    "ax = Fig.add_subplot(1,1,1)\n",
    "\n",
    "### Frequency\n",
    "Hist_Freq = Hist_Avg['Obs']\n",
    "ax.bar(Hist_Freq.index[0]*100,Hist_Freq[0]*100,align='edge',width=-Hist_Gap*100*0.45,color=MyGR.MyColor('Blue',1))\n",
    "ax.bar(Hist_Freq.index[1:].map(lambda x: x*100),Hist_Freq[1:]*100,align='edge',width=-Hist_Gap*100*0.45,color=MyGR.MyColor('Blue',0.5),label='Number of Observations')\n",
    "### Size\n",
    "Hist_Frac = Hist_Avg['Asset']\n",
    "ax.bar(Hist_Frac.index[0]*100,Hist_Frac[0]*100,align='edge',width=Hist_Gap*100*0.45,color=MyGR.MyColor('Red',1))\n",
    "ax.bar(Hist_Frac.index[1:].map(lambda x: x*100),Hist_Frac[1:]*100,align='edge',width=Hist_Gap*100*0.45,color=MyGR.MyColor('Red',0.5),label='Total Assets')\n",
    "\n",
    "\n",
    "MyGR.Setup_Ax(ax,XTickNbins=7,YTickNbins=7)\n",
    "plt.ylabel('Fraction of All Households (\\%)',fontsize=FontSize_1)\n",
    "plt.xlabel('Foreign Share (\\%)',fontsize=FontSize_1)\n",
    "ax.tick_params(labelsize=FontSize_1)\n",
    "plt.legend(fontsize=FontSize_2,loc='best',frameon=False)\n",
    "\n",
    "ax.vlines(CutoffShare*100,ax.get_ylim()[0],ax.get_ylim()[1], color=MyGR.MyColor('Black'),linestyles='dashed', linewidth=0.5)\n",
    "\n",
    "plt.tight_layout()\n",
    "# ax.set_rasterized(True)\n",
    "plt.savefig('TableGraph/Exposure_HH_AvgDist.eps', format='eps')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Average Statistics of Integrated Households at Different Levels of Cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 550x412.5 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Conditional Statistics of Integrated HHs\n",
    "Fig = MyGR.Setup_Fig(FigSize=FigSize)\n",
    "ax = Fig.add_subplot(1,1,1)\n",
    "\n",
    "### Fraction of Integrated Households\n",
    "Line_Main.Plot(Stat_IntHH_Avg.index*100,Stat_IntHH_Avg['Obs']*100,ax=ax,Label=\"Number of Obs.\")\n",
    "Line_0.Plot(Stat_IntHH_Avg.index*100,Stat_IntHH_Avg['Asset']*100,ax=ax,Label=\"Total Fin. Asset\")\n",
    "### Foreign Share of Integrated Households\n",
    "Line_1.Plot(Stat_IntHH_Avg.index*100,Stat_IntHH_Avg['Avg']*100,ax=ax,Label=\"Foreign Share: Mean\")\n",
    "Line_2.Plot(Stat_IntHH_Avg.index*100,Stat_IntHH_Avg[0.5]*100,ax=ax,Label=\"Foreign Share: Median\")\n",
    "### Setup the axis format\n",
    "MyGR.Setup_Ax(ax,XTickNbins=7,YTickNbins=7)\n",
    "plt.ylabel('Statistics of Integrated HHs (\\%)',fontsize=FontSize_1)\n",
    "plt.xlabel('Cutoff Foreign Share (\\%)',fontsize=FontSize_1)\n",
    "ax.tick_params(labelsize=FontSize_1)\n",
    "plt.legend(fontsize=FontSize_2,loc='best',frameon=False)\n",
    "\n",
    "ax.vlines(CutoffShare*100,ax.get_ylim()[0],ax.get_ylim()[1], color=MyGR.MyColor('Black'),linestyles='dashed')\n",
    "\n",
    "plt.tight_layout()\n",
    "# ax.set_rasterized(True)\n",
    "plt.savefig('TableGraph/Exposure_HH_Integrated.eps', format='eps')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average summary statistics for integrated households are\n",
      "Obs      0.332179\n",
      "0.25     0.122069\n",
      "0.5      0.155516\n",
      "0.75     0.192346\n",
      "Avg      0.211338\n",
      "Asset    0.327029\n",
      "Name: 0.1, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print('The average summary statistics for integrated households are')\n",
    "print(Stat_IntHH_Avg.loc[CutoffShare])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transition Probability of Integration Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sample of Households Showing up more than Once in CFM\n",
    "HhFreq = CFM['hldid'].value_counts().rename('Freq').reset_index().rename(columns={'index': 'hldid'})\n",
    "HhGroup = HhFreq.loc[HhFreq['Freq']>1,:]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    10101\n",
       "2    15095\n",
       "4    18413\n",
       "3    21667\n",
       "5    22099\n",
       "Name: Freq, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HhFreq['Freq'].value_counts(normalize=True).cumsum()\n",
    "HhFreq['Freq'].value_counts(normalize=False).cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "HhGroup = HhGroup[['hldid']].merge(right=CFM,how='left',on='hldid').sort_values(['hldid','period']).reset_index()\n",
    "HhGroup = HhGroup.merge(right=SumStat_TS['WAvg'],how='left',left_on='period',right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "HhGroup['MNum'] = HhGroup['period'].apply(lambda x: x.year*12+x.month)\n",
    "HhGroup['LagMNum'] = HhGroup.groupby('hldid')['MNum'].shift()\n",
    "HhGroup['MGap'] = HhGroup['MNum']-HhGroup['LagMNum']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0         1\n",
       "2.0        27\n",
       "3.0       105\n",
       "4.0       158\n",
       "5.0       185\n",
       "6.0       189\n",
       "7.0       212\n",
       "8.0       431\n",
       "9.0       825\n",
       "10.0     3975\n",
       "11.0     5784\n",
       "12.0    13136\n",
       "13.0    17476\n",
       "14.0    18559\n",
       "15.0    19159\n",
       "16.0    19445\n",
       "17.0    19677\n",
       "18.0    19794\n",
       "19.0    20111\n",
       "20.0    20599\n",
       "21.0    21656\n",
       "22.0    21929\n",
       "23.0    22028\n",
       "24.0    22210\n",
       "25.0    22416\n",
       "26.0    22489\n",
       "27.0    22552\n",
       "28.0    22613\n",
       "29.0    22678\n",
       "30.0    22845\n",
       "31.0    22898\n",
       "32.0    22939\n",
       "33.0    22998\n",
       "34.0    23036\n",
       "35.0    23055\n",
       "36.0    23070\n",
       "37.0    23085\n",
       "38.0    23099\n",
       "39.0    23107\n",
       "40.0    23111\n",
       "41.0    23119\n",
       "42.0    23146\n",
       "43.0    23170\n",
       "44.0    23179\n",
       "45.0    23182\n",
       "46.0    23184\n",
       "Name: MGap, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HhGroup['MGap'].value_counts(normalize=True).sort_index()\n",
    "HhGroup['MGap'].value_counts(normalize=False).sort_index().cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HhGroup['IntType'] = HhGroup[ExposureVar]>=HhGroup['WAvg']\n",
    "HhGroup['IntType'] = HhGroup[ExposureVar]>=0.1\n",
    "HhGroup['LagIntType'] = HhGroup.groupby('hldid')['IntType'].shift()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "HhGroup = HhGroup.dropna(subset=['IntType','LagIntType'])\n",
    "TrProb = HhGroup.groupby(['period','MGap','LagIntType','IntType'])['hldid'].count().unstack(level=['LagIntType','IntType'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-29-e6f09edaf36d>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  TrProb_NonInt['TotNum'] = TrProb_NonInt[False]+TrProb_NonInt[True]\n"
     ]
    }
   ],
   "source": [
    "TrProb_NonInt = TrProb[False]\n",
    "TrProb_NonInt['TotNum'] = TrProb_NonInt[False]+TrProb_NonInt[True]\n",
    "TrProb_NonInt['Persistence'] = TrProb_NonInt[False]/TrProb_NonInt['TotNum']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-30-50688645a999>:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  TrProb_Int['TotNum'] = TrProb_Int[False]+TrProb_Int[True]\n"
     ]
    }
   ],
   "source": [
    "TrProb_Int = TrProb[True]\n",
    "TrProb_Int['TotNum'] = TrProb_Int[False]+TrProb_Int[True]\n",
    "TrProb_Int['Persistence'] = TrProb_Int[True]/TrProb_Int['TotNum']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "Pers_Int = TrProb_Int['Persistence'].mean(level=1)[12]\n",
    "Pers_NonInt = TrProb_NonInt['Persistence'].mean(level=1)[12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.73463571, 0.2258699 ],\n",
       "       [0.26536429, 0.7741301 ]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TrProbMat_A = np.array([[Pers_Int,1-Pers_NonInt],[1-Pers_Int,Pers_NonInt] ])\n",
    "TrProbMat_A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "TrProbMat_Q = fractional_matrix_power(TrProbMat_A,1/4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.91603036, 0.07147237],\n",
       "       [0.08396964, 0.92852763]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TrProbMat_Q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.92580177, 0.68938938],\n",
       "       [0.71772921, 0.9380014 ]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TrProbMat_A**(1/4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Change of Asset Positions when Switching"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sample of Households Showing up more than Once in CFM\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "HhFreq = CFM['hldid'].value_counts().rename('Freq').reset_index().rename(columns={'index': 'hldid'})\n",
    "HhGroup = HhFreq.loc[HhFreq['Freq']>1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "HhGroup = HhGroup[['hldid']].merge(right=CFM,how='left',on='hldid').sort_values(['hldid','period']).reset_index()\n",
    "HhGroup = HhGroup.merge(right=SumStat_TS['WAvg'],how='left',left_on='period',right_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "HhGroup['MNum'] = HhGroup['period'].apply(lambda x: x.year*12+x.month)\n",
    "HhGroup['LagMNum'] = HhGroup.groupby('hldid')['MNum'].shift()\n",
    "HhGroup['MGap'] = HhGroup['MNum']-HhGroup['LagMNum']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "HhGroup['IntType'] = HhGroup[ExposureVar]>=0.1\n",
    "HhGroup['LagIntType'] = HhGroup.groupby('hldid')['IntType'].shift()\n",
    "HhGroup['LagExposure'] = HhGroup.groupby('hldid')[ExposureVar].shift()\n",
    "HhGroup['DiffExposure'] = HhGroup[ExposureVar]-HhGroup['LagExposure']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def TempFun_WeightedHistogram(DS,Var_V,Var_W,Var_G):\n",
    "    Var_W = Var_W if isinstance(Var_W,list) else [Var_W]\n",
    "    TempVarList = [Var_V,Var_G]+Var_W \n",
    "    TempDS = DS.loc[:,TempVarList].dropna()\n",
    "    \n",
    "    TempDS['W'] = TempDS[Var_W].prod(axis=1)\n",
    "    TempDS['W'] = TempDS['W']/TempDS['W'].sum()\n",
    "    TempDS['VW'] = TempDS[Var_V]*TempDS['W']\n",
    "\n",
    "    Hist = TempDS.groupby(Var_G)['VW'].sum()\n",
    "    Hist = Hist/Hist.sum()\n",
    "\n",
    "    return Hist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## From Integrated to Non-integrated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-21-14c38fd57192>:7: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  TempDS['ExposureGroup'] = pd.cut(TempDS['DiffExposure'],bins=TempBins,labels=TempLabels)\n",
      "<ipython-input-21-14c38fd57192>:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  TempDS['WW'] = 1\n"
     ]
    }
   ],
   "source": [
    "Temp_Int2NonInt = (HhGroup['MGap']==12) & (HhGroup['LagIntType']==True) & (HhGroup['IntType']==False)\n",
    "TempDS = HhGroup.loc[Temp_Int2NonInt,:]\n",
    "\n",
    "Hist_Gap = 0.05\n",
    "TempBins = [-np.inf]+list(np.linspace(-1-Hist_Gap,0+Hist_Gap,num=int(1/Hist_Gap)+3))+[np.inf]\n",
    "TempLabels = TempBins[1:-1]+[0.05+Hist_Gap]\n",
    "TempDS['ExposureGroup'] = pd.cut(TempDS['DiffExposure'],bins=TempBins,labels=TempLabels)\n",
    "TempDS['WW'] = 1\n",
    "\n",
    "TempResults = [TempFun_WeightedHistogram(TempDS.loc[TempDS[IdxTime]==TempDate,:], 'Obs', 'WW', 'ExposureGroup') \\\n",
    "               for TempDate in DateList]\n",
    "\n",
    "Hist_Obs = pd.DataFrame(TempResults,index=DateList).mean()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Figure\n",
    "Line_Main = MyGR.Line(MyGR.MyColor('Black'),'solid',3)\n",
    "Line_0 = MyGR.Line(MyGR.MyColor('Gray'),'dashed',2)\n",
    "Line_1 = MyGR.Line(MyGR.MyColor('Blue'),'dashed',1.5)\n",
    "Line_2 = MyGR.Line(MyGR.MyColor('Red'),'dashed',1.5)\n",
    "\n",
    "FigSize = (1/3,1/4)\n",
    "FontSize_1 = 6\n",
    "FontSize_2 = 4\n",
    "\n",
    "CutoffShare = 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 550x412.5 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Histogram (Average Distribution)\n",
    "Fig = MyGR.Setup_Fig(FigSize=FigSize)\n",
    "ax = Fig.add_subplot(1,1,1)\n",
    "\n",
    "### Frequency\n",
    "ax.bar(Hist_Obs.index[0]*100,Hist_Obs[0]*100,align='edge',width=-Hist_Gap*100*0.90,color=MyGR.MyColor('Blue',1))\n",
    "ax.bar(Hist_Obs.index[1:].map(lambda x: x*100),Hist_Obs[1:]*100,align='edge',width=-Hist_Gap*100*0.90,color=MyGR.MyColor('Blue',0.5),label='Number of Observations')\n",
    "\n",
    "MyGR.Setup_Ax(ax,XTickNbins=7,YTickNbins=7)\n",
    "plt.ylabel('Fraction of Households (\\%)',fontsize=FontSize_1)\n",
    "plt.xlabel('Change in Foreign Share (\\%)',fontsize=FontSize_1)\n",
    "ax.tick_params(labelsize=FontSize_1)\n",
    "plt.legend(fontsize=FontSize_2,loc='best',frameon=False)\n",
    "\n",
    "plt.tight_layout()\n",
    "ax.set_rasterized(True)\n",
    "plt.savefig('TableGraph/ChangeOfExposureInTransition_Int2NonInt_HH_AvgDist.eps', format='eps')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## From Non-integrated to Integrated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-24-e93cf89b2f3d>:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  TempDS['ExposureGroup'] = pd.cut(TempDS['DiffExposure'],bins=TempBins,labels=TempLabels)\n",
      "<ipython-input-24-e93cf89b2f3d>:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  TempDS['WW'] = 1\n"
     ]
    }
   ],
   "source": [
    "Temp_NonInt2Int = (HhGroup['MGap']==12) & (HhGroup['LagIntType']==False) & (HhGroup['IntType']==True)\n",
    "TempDS = HhGroup.loc[Temp_NonInt2Int,:]\n",
    "\n",
    "Hist_Gap = 0.05\n",
    "Val_Min = 0\n",
    "Val_Max = 1\n",
    "TempBins = [-np.inf]+list(np.linspace(Val_Min-Hist_Gap,Val_Max+Hist_Gap,num=int(1/Hist_Gap)+3))+[np.inf]\n",
    "TempLabels = TempBins[1:-1]+[Val_Max+Hist_Gap*2]\n",
    "TempDS['ExposureGroup'] = pd.cut(TempDS['DiffExposure'],bins=TempBins,labels=TempLabels)\n",
    "TempDS['WW'] = 1\n",
    "\n",
    "TempResults = [TempFun_WeightedHistogram(TempDS.loc[TempDS[IdxTime]==TempDate,:], 'Obs', 'WW', 'ExposureGroup') \\\n",
    "               for TempDate in DateList]\n",
    "\n",
    "Hist_Obs = pd.DataFrame(TempResults,index=DateList).mean()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 550x412.5 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Histogram (Average Distribution)\n",
    "Fig = MyGR.Setup_Fig(FigSize=FigSize)\n",
    "ax = Fig.add_subplot(1,1,1)\n",
    "\n",
    "### Frequency\n",
    "ax.bar(Hist_Obs.index[0]*100,Hist_Obs[0]*100,align='edge',width=-Hist_Gap*100*0.90,color=MyGR.MyColor('Blue',1))\n",
    "ax.bar(Hist_Obs.index[1:].map(lambda x: x*100),Hist_Obs[1:]*100,align='edge',width=-Hist_Gap*100*0.90,color=MyGR.MyColor('Blue',0.5),label='Number of Observations')\n",
    "\n",
    "MyGR.Setup_Ax(ax,XTickNbins=7,YTickNbins=7)\n",
    "plt.ylabel('Fraction of Households (\\%)',fontsize=FontSize_1)\n",
    "plt.xlabel('Change in Foreign Share (\\%)',fontsize=FontSize_1)\n",
    "ax.tick_params(labelsize=FontSize_1)\n",
    "plt.legend(fontsize=FontSize_2,loc='best',frameon=False)\n",
    "\n",
    "plt.tight_layout()\n",
    "ax.set_rasterized(True)\n",
    "plt.savefig('TableGraph/ChangeOfExposureInTransition_NonInt2Int_HH_AvgDist.eps', format='eps')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution Conditional on Integrated Status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Read in the Data\n",
    "VL = pickle.load(open('../Data/CFM_GQ/CFM_Cleaned.p','rb'))['ValueLabel']\n",
    "\n",
    "## Function to Query the Value Label\n",
    "def Fun_ValueLabelQuery(VarName, Code, VLDS=VL):\n",
    "    \"\"\"\n",
    "    VarName: Name of Variables to be Queried\n",
    "    Code: Code of the Variable \n",
    "    VLDS: Dataset of the Value Labels\n",
    "    \"\"\"\n",
    "    \n",
    "    if VarName in VLDS.index:\n",
    "        Temp = VLDS.loc[VarName,:]\n",
    "        CodeList = [Code] if  isinstance(Code, (int,float)) else Code\n",
    "        \n",
    "        SearchIdx = set(CodeList) & set(Temp.index)\n",
    "        if len(SearchIdx)<len(CodeList):\n",
    "            Warning('There are codes without labels.')\n",
    "        if len(SearchIdx)>0:\n",
    "            LabelList = [Temp.loc[ii,'Value'] if ii in SearchIdx else '' for ii in CodeList ]\n",
    "            if isinstance(Code,(int,float)):\n",
    "                return LabelList[0]\n",
    "            else:\n",
    "                return LabelList\n",
    "    else:\n",
    "        return Code\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['period', 'hldid', 'Asset_Total', 'Asset_Foreign_1', 'Asset_Foreign_2',\n",
       "       'Asset_Foreign_3', 'Exposure_1', 'Exposure_2', 'Exposure_3', 'Weight',\n",
       "       'QDate', 'Obs'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CFM.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['WorkStatus_MaleHead', 'Weight', 'WorkStatus_FemalHead', 'Age_Head',\n",
       "       'Marry', 'Edu_FemalHead', 'Income_NW', 'Region', 'HhComposition',\n",
       "       'HhSize', 'OwnRent', 'Income_W', 'CitySize', 'Edu_MaleHead'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "HH.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "TempList_CFM = ['period','hldid','Asset_Total','QDate','Obs',ExposureVar]\n",
    "TempList_HH = ['WorkStatus_MaleHead', 'Weight', 'WorkStatus_FemalHead', 'Age_Head',\n",
    "                'Marry', 'Edu_FemalHead', 'Income_NW', 'Region', 'HhComposition',\n",
    "                'HhSize', 'OwnRent', 'Income_W', 'CitySize', 'Edu_MaleHead']\n",
    "TempDS = CFM[TempList_CFM].merge(HH[TempList_HH],how='left',on='hldid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def WeightedTab(DS,Var):\n",
    "    Var_Time = 'period'\n",
    "    Var_Tab = 'Obs'\n",
    "    Var_Weight = 'Weight'\n",
    "    TempDS = DS.loc[:,[Var, Var_Tab, Var_Weight, Var_Time]]\n",
    "    TempDS['VW'] = TempDS[Var_Tab]*TempDS[Var_Weight]\n",
    "    VW = TempDS.groupby([Var_Time, Var])['VW'].sum().unstack()\n",
    "    W = TempDS.groupby(Var_Time)[Var_Weight].sum()\n",
    "    V = VW.div(W,axis=0)\n",
    "    \n",
    "    return V\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def WeightedTabByVars(DS,Var,ByVars):\n",
    "    Var_Tab = 'Obs'\n",
    "    Var_Weight = 'Weight'\n",
    "    TempDS = DS.loc[:,ByVars+[Var, Var_Tab, Var_Weight]]\n",
    "    TempDS['VW'] = TempDS[Var_Tab]*TempDS[Var_Weight]\n",
    "    VW = TempDS.groupby(ByVars+[Var])['VW'].sum().unstack()\n",
    "    W = TempDS.groupby(ByVars+[Var])[Var_Weight].sum().unstack().sum(axis=1)\n",
    "    V = VW.div(W,axis=0)\n",
    "    \n",
    "    return V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def TempFun_SumStat(DS,Var):\n",
    "    Temp = WeightedTab(DS,Var).sort_index(axis=1)\n",
    "    Temp = Temp.rename(columns={xx: Fun_ValueLabelQuery(Var,xx) for xx in Temp.columns})\n",
    "    return Temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "TempDS['Exposure_Group'] = TempDS[ExposureVar]>=0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "Temp_2 = WeightedTabByVars(TempDS, 'Region',['period']).reset_index()\n",
    "Temp_2['Exposure_Group'] = 'FullSample'\n",
    "Temp_2 = Temp_2.set_index(['Exposure_Group', 'period'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exposure_Group</th>\n",
       "      <th>period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">False</th>\n",
       "      <th>2014-10-01</th>\n",
       "      <td>0.143519</td>\n",
       "      <td>0.089598</td>\n",
       "      <td>0.042177</td>\n",
       "      <td>0.036928</td>\n",
       "      <td>0.366916</td>\n",
       "      <td>0.246007</td>\n",
       "      <td>0.024959</td>\n",
       "      <td>0.008296</td>\n",
       "      <td>0.035593</td>\n",
       "      <td>0.006008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01</th>\n",
       "      <td>0.114862</td>\n",
       "      <td>0.110739</td>\n",
       "      <td>0.021221</td>\n",
       "      <td>0.027674</td>\n",
       "      <td>0.343658</td>\n",
       "      <td>0.278735</td>\n",
       "      <td>0.030785</td>\n",
       "      <td>0.007745</td>\n",
       "      <td>0.044384</td>\n",
       "      <td>0.020198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01</th>\n",
       "      <td>0.177339</td>\n",
       "      <td>0.090983</td>\n",
       "      <td>0.046471</td>\n",
       "      <td>0.025955</td>\n",
       "      <td>0.326022</td>\n",
       "      <td>0.272622</td>\n",
       "      <td>0.012398</td>\n",
       "      <td>0.006903</td>\n",
       "      <td>0.028245</td>\n",
       "      <td>0.013062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>0.109327</td>\n",
       "      <td>0.104856</td>\n",
       "      <td>0.029445</td>\n",
       "      <td>0.045473</td>\n",
       "      <td>0.361731</td>\n",
       "      <td>0.280817</td>\n",
       "      <td>0.034980</td>\n",
       "      <td>0.005868</td>\n",
       "      <td>0.017872</td>\n",
       "      <td>0.009632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01</th>\n",
       "      <td>0.117866</td>\n",
       "      <td>0.121826</td>\n",
       "      <td>0.026662</td>\n",
       "      <td>0.029446</td>\n",
       "      <td>0.381122</td>\n",
       "      <td>0.276431</td>\n",
       "      <td>0.024400</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>0.016503</td>\n",
       "      <td>0.005638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">True</th>\n",
       "      <th>2018-08-01</th>\n",
       "      <td>0.172236</td>\n",
       "      <td>0.099863</td>\n",
       "      <td>0.029566</td>\n",
       "      <td>0.026014</td>\n",
       "      <td>0.428454</td>\n",
       "      <td>0.163622</td>\n",
       "      <td>0.003987</td>\n",
       "      <td>0.006499</td>\n",
       "      <td>0.059107</td>\n",
       "      <td>0.010652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-01</th>\n",
       "      <td>0.152652</td>\n",
       "      <td>0.112646</td>\n",
       "      <td>0.049986</td>\n",
       "      <td>0.035792</td>\n",
       "      <td>0.423044</td>\n",
       "      <td>0.133637</td>\n",
       "      <td>0.014212</td>\n",
       "      <td>0.003057</td>\n",
       "      <td>0.067600</td>\n",
       "      <td>0.007373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>0.100834</td>\n",
       "      <td>0.113284</td>\n",
       "      <td>0.039069</td>\n",
       "      <td>0.027410</td>\n",
       "      <td>0.428126</td>\n",
       "      <td>0.228403</td>\n",
       "      <td>0.021246</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022957</td>\n",
       "      <td>0.018669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>0.170237</td>\n",
       "      <td>0.121148</td>\n",
       "      <td>0.026447</td>\n",
       "      <td>0.025859</td>\n",
       "      <td>0.441831</td>\n",
       "      <td>0.166225</td>\n",
       "      <td>0.019008</td>\n",
       "      <td>0.001816</td>\n",
       "      <td>0.018167</td>\n",
       "      <td>0.009262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>0.117856</td>\n",
       "      <td>0.129933</td>\n",
       "      <td>0.028781</td>\n",
       "      <td>0.026682</td>\n",
       "      <td>0.460120</td>\n",
       "      <td>0.165568</td>\n",
       "      <td>0.010185</td>\n",
       "      <td>0.003527</td>\n",
       "      <td>0.035578</td>\n",
       "      <td>0.021768</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Region                           1         2         3         4         5   \\\n",
       "Exposure_Group period                                                         \n",
       "False          2014-10-01  0.143519  0.089598  0.042177  0.036928  0.366916   \n",
       "               2014-11-01  0.114862  0.110739  0.021221  0.027674  0.343658   \n",
       "               2014-12-01  0.177339  0.090983  0.046471  0.025955  0.326022   \n",
       "               2015-01-01  0.109327  0.104856  0.029445  0.045473  0.361731   \n",
       "               2015-02-01  0.117866  0.121826  0.026662  0.029446  0.381122   \n",
       "...                             ...       ...       ...       ...       ...   \n",
       "True           2018-08-01  0.172236  0.099863  0.029566  0.026014  0.428454   \n",
       "               2018-09-01  0.152652  0.112646  0.049986  0.035792  0.423044   \n",
       "               2018-10-01  0.100834  0.113284  0.039069  0.027410  0.428126   \n",
       "               2018-11-01  0.170237  0.121148  0.026447  0.025859  0.441831   \n",
       "               2018-12-01  0.117856  0.129933  0.028781  0.026682  0.460120   \n",
       "\n",
       "Region                           6         7         8         9         10  \n",
       "Exposure_Group period                                                        \n",
       "False          2014-10-01  0.246007  0.024959  0.008296  0.035593  0.006008  \n",
       "               2014-11-01  0.278735  0.030785  0.007745  0.044384  0.020198  \n",
       "               2014-12-01  0.272622  0.012398  0.006903  0.028245  0.013062  \n",
       "               2015-01-01  0.280817  0.034980  0.005868  0.017872  0.009632  \n",
       "               2015-02-01  0.276431  0.024400  0.000106  0.016503  0.005638  \n",
       "...                             ...       ...       ...       ...       ...  \n",
       "True           2018-08-01  0.163622  0.003987  0.006499  0.059107  0.010652  \n",
       "               2018-09-01  0.133637  0.014212  0.003057  0.067600  0.007373  \n",
       "               2018-10-01  0.228403  0.021246  0.000000  0.022957  0.018669  \n",
       "               2018-11-01  0.166225  0.019008  0.001816  0.018167  0.009262  \n",
       "               2018-12-01  0.165568  0.010185  0.003527  0.035578  0.021768  \n",
       "\n",
       "[102 rows x 10 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Temp_1 = WeightedTabByVars(TempDS, 'Region', ['Exposure_Group', 'period']).fillna(0)\n",
    "Temp_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exposure_Group</th>\n",
       "      <th>period</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">False</th>\n",
       "      <th>2014-10-01</th>\n",
       "      <td>0.143519</td>\n",
       "      <td>0.089598</td>\n",
       "      <td>0.042177</td>\n",
       "      <td>0.036928</td>\n",
       "      <td>0.366916</td>\n",
       "      <td>0.246007</td>\n",
       "      <td>0.024959</td>\n",
       "      <td>0.008296</td>\n",
       "      <td>0.035593</td>\n",
       "      <td>0.006008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-11-01</th>\n",
       "      <td>0.114862</td>\n",
       "      <td>0.110739</td>\n",
       "      <td>0.021221</td>\n",
       "      <td>0.027674</td>\n",
       "      <td>0.343658</td>\n",
       "      <td>0.278735</td>\n",
       "      <td>0.030785</td>\n",
       "      <td>0.007745</td>\n",
       "      <td>0.044384</td>\n",
       "      <td>0.020198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-01</th>\n",
       "      <td>0.177339</td>\n",
       "      <td>0.090983</td>\n",
       "      <td>0.046471</td>\n",
       "      <td>0.025955</td>\n",
       "      <td>0.326022</td>\n",
       "      <td>0.272622</td>\n",
       "      <td>0.012398</td>\n",
       "      <td>0.006903</td>\n",
       "      <td>0.028245</td>\n",
       "      <td>0.013062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>0.109327</td>\n",
       "      <td>0.104856</td>\n",
       "      <td>0.029445</td>\n",
       "      <td>0.045473</td>\n",
       "      <td>0.361731</td>\n",
       "      <td>0.280817</td>\n",
       "      <td>0.034980</td>\n",
       "      <td>0.005868</td>\n",
       "      <td>0.017872</td>\n",
       "      <td>0.009632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01</th>\n",
       "      <td>0.117866</td>\n",
       "      <td>0.121826</td>\n",
       "      <td>0.026662</td>\n",
       "      <td>0.029446</td>\n",
       "      <td>0.381122</td>\n",
       "      <td>0.276431</td>\n",
       "      <td>0.024400</td>\n",
       "      <td>0.000106</td>\n",
       "      <td>0.016503</td>\n",
       "      <td>0.005638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">FullSample</th>\n",
       "      <th>2018-08-01</th>\n",
       "      <td>0.138600</td>\n",
       "      <td>0.113509</td>\n",
       "      <td>0.034629</td>\n",
       "      <td>0.030510</td>\n",
       "      <td>0.359128</td>\n",
       "      <td>0.267190</td>\n",
       "      <td>0.006637</td>\n",
       "      <td>0.004564</td>\n",
       "      <td>0.032631</td>\n",
       "      <td>0.012603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-09-01</th>\n",
       "      <td>0.147822</td>\n",
       "      <td>0.102737</td>\n",
       "      <td>0.041473</td>\n",
       "      <td>0.018466</td>\n",
       "      <td>0.385578</td>\n",
       "      <td>0.240454</td>\n",
       "      <td>0.011207</td>\n",
       "      <td>0.007661</td>\n",
       "      <td>0.029896</td>\n",
       "      <td>0.014706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-10-01</th>\n",
       "      <td>0.095841</td>\n",
       "      <td>0.147390</td>\n",
       "      <td>0.028635</td>\n",
       "      <td>0.020502</td>\n",
       "      <td>0.339815</td>\n",
       "      <td>0.311104</td>\n",
       "      <td>0.019882</td>\n",
       "      <td>0.001393</td>\n",
       "      <td>0.020403</td>\n",
       "      <td>0.015036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-11-01</th>\n",
       "      <td>0.161162</td>\n",
       "      <td>0.108282</td>\n",
       "      <td>0.025229</td>\n",
       "      <td>0.023823</td>\n",
       "      <td>0.388816</td>\n",
       "      <td>0.246125</td>\n",
       "      <td>0.017115</td>\n",
       "      <td>0.001120</td>\n",
       "      <td>0.021669</td>\n",
       "      <td>0.006659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-12-01</th>\n",
       "      <td>0.118936</td>\n",
       "      <td>0.124576</td>\n",
       "      <td>0.034664</td>\n",
       "      <td>0.044682</td>\n",
       "      <td>0.366598</td>\n",
       "      <td>0.253602</td>\n",
       "      <td>0.009786</td>\n",
       "      <td>0.005254</td>\n",
       "      <td>0.029496</td>\n",
       "      <td>0.012407</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>153 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Region                           1         2         3         4         5   \\\n",
       "Exposure_Group period                                                         \n",
       "False          2014-10-01  0.143519  0.089598  0.042177  0.036928  0.366916   \n",
       "               2014-11-01  0.114862  0.110739  0.021221  0.027674  0.343658   \n",
       "               2014-12-01  0.177339  0.090983  0.046471  0.025955  0.326022   \n",
       "               2015-01-01  0.109327  0.104856  0.029445  0.045473  0.361731   \n",
       "               2015-02-01  0.117866  0.121826  0.026662  0.029446  0.381122   \n",
       "...                             ...       ...       ...       ...       ...   \n",
       "FullSample     2018-08-01  0.138600  0.113509  0.034629  0.030510  0.359128   \n",
       "               2018-09-01  0.147822  0.102737  0.041473  0.018466  0.385578   \n",
       "               2018-10-01  0.095841  0.147390  0.028635  0.020502  0.339815   \n",
       "               2018-11-01  0.161162  0.108282  0.025229  0.023823  0.388816   \n",
       "               2018-12-01  0.118936  0.124576  0.034664  0.044682  0.366598   \n",
       "\n",
       "Region                           6         7         8         9         10  \n",
       "Exposure_Group period                                                        \n",
       "False          2014-10-01  0.246007  0.024959  0.008296  0.035593  0.006008  \n",
       "               2014-11-01  0.278735  0.030785  0.007745  0.044384  0.020198  \n",
       "               2014-12-01  0.272622  0.012398  0.006903  0.028245  0.013062  \n",
       "               2015-01-01  0.280817  0.034980  0.005868  0.017872  0.009632  \n",
       "               2015-02-01  0.276431  0.024400  0.000106  0.016503  0.005638  \n",
       "...                             ...       ...       ...       ...       ...  \n",
       "FullSample     2018-08-01  0.267190  0.006637  0.004564  0.032631  0.012603  \n",
       "               2018-09-01  0.240454  0.011207  0.007661  0.029896  0.014706  \n",
       "               2018-10-01  0.311104  0.019882  0.001393  0.020403  0.015036  \n",
       "               2018-11-01  0.246125  0.017115  0.001120  0.021669  0.006659  \n",
       "               2018-12-01  0.253602  0.009786  0.005254  0.029496  0.012407  \n",
       "\n",
       "[153 rows x 10 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([Temp_1,Temp_2],axis=0).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary statistics for Region... Done.\n",
      "Summary statistics for Marry... Done.\n",
      "Summary statistics for WorkStatus_MaleHead... Done.\n",
      "Summary statistics for WorkStatus_FemalHead... Done.\n",
      "Summary statistics for Edu_MaleHead... Done.\n",
      "Summary statistics for Edu_FemalHead... Done.\n",
      "Summary statistics for Income_W... Done.\n",
      "Summary statistics for HhSize... Done.\n",
      "Summary statistics for OwnRent... Done.\n",
      "Summary statistics for Age_Head... Done.\n"
     ]
    }
   ],
   "source": [
    "SumStat = {}\n",
    "for Var in ['Region', 'Marry', 'WorkStatus_MaleHead', 'WorkStatus_FemalHead', \\\n",
    "            'Edu_MaleHead','Edu_FemalHead', 'Income_W', 'HhSize', 'OwnRent', 'Age_Head']:\n",
    "    print('Summary statistics for '+Var+'... ', end='')\n",
    "    Stat_1 = WeightedTabByVars(TempDS, Var, ['period']).reset_index()\n",
    "    Stat_1['Exposure_Group'] = 'FullSample'\n",
    "    Stat_1.set_index(['Exposure_Group', 'period'],inplace=True)\n",
    "\n",
    "    Stat_2 = WeightedTabByVars(TempDS, Var, ['Exposure_Group', 'period'])\n",
    "\n",
    "    SumStat[Var] = pd.concat([Stat_1,Stat_2], axis=0).sort_index().unstack(level='Exposure_Group')\n",
    "    SumStat[Var].columns = SumStat[Var].columns.swaplevel(0,1)\n",
    "    SumStat[Var].sort_index(axis=1, inplace=True)\n",
    "    print('Done.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "DateLimit = (datetime.datetime(2014,10,1),datetime.datetime(2018,12,31))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average summary statistics for  Region  is \n",
      "Exposure_Group        Non-integrated  Integrated  FullSample\n",
      "Region                                                      \n",
      "BRITISH COLUMBIA            0.122862    0.153519    0.132593\n",
      "ALBERTA                     0.101101    0.114864    0.106401\n",
      "SASKATCHEWAN                0.032040    0.031753    0.032372\n",
      "MANITOBA                    0.037252    0.029496    0.034419\n",
      "ONTARIO                     0.335957    0.403906    0.360610\n",
      "QUEBEC                      0.309038    0.162819    0.258556\n",
      "NEW BRUNSWICK               0.019039    0.025387    0.020834\n",
      "PRINCE EDWARD ISLAND        0.004317    0.006202    0.004918\n",
      "NOVA SCOTIA                 0.028602    0.044775    0.034120\n",
      "NEWFOUNDLAND                0.009792    0.027278    0.015177\n",
      "\n",
      "\n",
      "The average summary statistics for  Marry  is \n",
      "Exposure_Group              Non-integrated  Integrated  FullSample\n",
      "Marry                                                             \n",
      "Never  Married                    0.194856    0.182564    0.191541\n",
      "Married                           0.498100    0.538000    0.511490\n",
      "Common Law                        0.126802    0.106760    0.119630\n",
      "Divorced/Seperated/Widowed        0.180242    0.172675    0.177339\n",
      "\n",
      "\n",
      "The average summary statistics for  WorkStatus_MaleHead  is \n",
      "Exposure_Group                                      Non-integrated  \\\n",
      "WorkStatus_MaleHead                                                  \n",
      "Full-time                                                 0.560466   \n",
      "Part-time                                                 0.073682   \n",
      "Temporarily unemployed(looking for work)                  0.053605   \n",
      "Retired                                                   0.243430   \n",
      "Homemaker                                                 0.010132   \n",
      "Full-time student                                         0.031565   \n",
      "Not in the labour force (Permanently disabled, ...        0.027120   \n",
      "\n",
      "Exposure_Group                                      Integrated  FullSample  \n",
      "WorkStatus_MaleHead                                                         \n",
      "Full-time                                             0.550094    0.557572  \n",
      "Part-time                                             0.078378    0.076005  \n",
      "Temporarily unemployed(looking for work)              0.062083    0.056590  \n",
      "Retired                                               0.241200    0.241115  \n",
      "Homemaker                                             0.013018    0.010906  \n",
      "Full-time student                                     0.026568    0.029734  \n",
      "Not in the labour force (Permanently disabled, ...    0.028658    0.028079  \n",
      "\n",
      "\n",
      "The average summary statistics for  WorkStatus_FemalHead  is \n",
      "Exposure_Group                                      Non-integrated  \\\n",
      "WorkStatus_FemalHead                                                 \n",
      "Full-time                                                 0.446364   \n",
      "Part-time                                                 0.197813   \n",
      "Temporarily unemployed(looking for work)                  0.063612   \n",
      "Retired                                                   0.183710   \n",
      "Homemaker                                                 0.067356   \n",
      "Full-time student                                         0.020764   \n",
      "Not in the labour force (Permanently disabled, ...        0.020380   \n",
      "\n",
      "Exposure_Group                                      Integrated  FullSample  \n",
      "WorkStatus_FemalHead                                                        \n",
      "Full-time                                             0.447574    0.447928  \n",
      "Part-time                                             0.194856    0.196140  \n",
      "Temporarily unemployed(looking for work)              0.069323    0.066140  \n",
      "Retired                                               0.179406    0.181176  \n",
      "Homemaker                                             0.063833    0.066248  \n",
      "Full-time student                                     0.022109    0.021118  \n",
      "Not in the labour force (Permanently disabled, ...    0.022899    0.021249  \n",
      "\n",
      "\n",
      "The average summary statistics for  Edu_MaleHead  is \n",
      "Exposure_Group                               Non-integrated  Integrated  \\\n",
      "Edu_MaleHead                                                              \n",
      "Less than Grade 9                                  0.044519    0.028083   \n",
      "Grades 9 through 13                                0.253512    0.223910   \n",
      "Some Community College or University               0.150988    0.162554   \n",
      "Community College Certificate/Diploma/CEGEP        0.216010    0.216174   \n",
      "University  Undergraduate Degree                   0.216202    0.233376   \n",
      "Post-Graduate Degree                               0.115133    0.132850   \n",
      "Trade school certificate or diploma                0.003636    0.003054   \n",
      "\n",
      "Exposure_Group                               FullSample  \n",
      "Edu_MaleHead                                             \n",
      "Less than Grade 9                              0.038909  \n",
      "Grades 9 through 13                            0.242318  \n",
      "Some Community College or University           0.153659  \n",
      "Community College Certificate/Diploma/CEGEP    0.217784  \n",
      "University  Undergraduate Degree               0.222255  \n",
      "Post-Graduate Degree                           0.121692  \n",
      "Trade school certificate or diploma            0.003383  \n",
      "\n",
      "\n",
      "The average summary statistics for  Edu_FemalHead  is \n",
      "Exposure_Group                               Non-integrated  Integrated  \\\n",
      "Edu_FemalHead                                                             \n",
      "Less than Grade 9                                  0.022937    0.015069   \n",
      "Grades 9 through 13                                0.254447    0.240702   \n",
      "Some Community College or University               0.146488    0.164437   \n",
      "Community College Certificate/Diploma/CEGEP        0.242163    0.247745   \n",
      "University  Undergraduate Degree                   0.242348    0.236427   \n",
      "Post-Graduate Degree                               0.089873    0.093563   \n",
      "Trade school certificate or diploma                0.001743    0.002058   \n",
      "\n",
      "Exposure_Group                               FullSample  \n",
      "Edu_FemalHead                                            \n",
      "Less than Grade 9                              0.020256  \n",
      "Grades 9 through 13                            0.248927  \n",
      "Some Community College or University           0.151515  \n",
      "Community College Certificate/Diploma/CEGEP    0.245642  \n",
      "University  Undergraduate Degree               0.240250  \n",
      "Post-Graduate Degree                           0.091567  \n",
      "Trade school certificate or diploma            0.001842  \n",
      "\n",
      "\n",
      "The average summary statistics for  Income_W  is \n",
      "Exposure_Group      Non-integrated  Integrated  FullSample\n",
      "Income_W                                                  \n",
      "Under $15,000             0.069936    0.062499    0.067662\n",
      "$15,000 - 19,999          0.050403    0.047501    0.048976\n",
      "$20,000 - 24,999          0.054971    0.050882    0.053197\n",
      "$25,000 - 29,999          0.045963    0.043376    0.045028\n",
      "$30,000 - 34,999          0.053358    0.052779    0.052960\n",
      "$35,000 - 44,999          0.103916    0.088787    0.099555\n",
      "$45,000 - 54,999          0.088482    0.085406    0.087426\n",
      "$55,000 - 59,999          0.042437    0.040580    0.041655\n",
      "$60,000 - 69,999          0.079022    0.075206    0.077467\n",
      "$70,000 - 99,999          0.212183    0.233922    0.219524\n",
      "$100,000 - 149,999        0.142745    0.154667    0.147112\n",
      "$150,000 +                0.056585    0.064393    0.059437\n",
      "\n",
      "\n",
      "The average summary statistics for  HhSize  is \n",
      "Exposure_Group  Non-integrated  Integrated  FullSample\n",
      "HhSize                                                \n",
      "1                     0.291982    0.270231    0.284479\n",
      "2                     0.352624    0.361542    0.354174\n",
      "3                     0.150230    0.139470    0.147506\n",
      "4                     0.145020    0.162629    0.151285\n",
      "5                     0.040342    0.045569    0.042637\n",
      "6                     0.013689    0.015063    0.014147\n",
      "7                     0.003450    0.002639    0.003102\n",
      "8                     0.002663    0.002856    0.002671\n",
      "\n",
      "\n",
      "The average summary statistics for  OwnRent  is \n",
      "Exposure_Group  Non-integrated  Integrated  FullSample\n",
      "OwnRent                                               \n",
      "Own                   0.727088    0.730416    0.728821\n",
      "Rent                  0.272912    0.269584    0.271179\n",
      "\n",
      "\n",
      "The average summary statistics for  Age_Head  is \n",
      "Exposure_Group  Non-integrated  Integrated  FullSample\n",
      "Age_Head                                              \n",
      "18                    0.000322    0.000126    0.000261\n",
      "19                    0.000481    0.000425    0.000470\n",
      "20                    0.000697    0.001072    0.000819\n",
      "21                    0.001379    0.001331    0.001382\n",
      "22                    0.002216    0.001789    0.002148\n",
      "...                        ...         ...         ...\n",
      "95                    0.000137    0.000061    0.000113\n",
      "96                    0.000161    0.000021    0.000114\n",
      "97                    0.000092    0.000128    0.000095\n",
      "98                    0.000021    0.000126    0.000046\n",
      "99                    0.000228    0.000352    0.000252\n",
      "\n",
      "[82 rows x 3 columns]\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "AvgSumStatDict = {}\n",
    "for Var in SumStat.keys():\n",
    "    TempInd = (SumStat[Var].index>=DateLimit[0]) & (SumStat[Var].index<=DateLimit[1])\n",
    "    AvgSumStatDict[Var] = SumStat[Var].loc[TempInd,:].fillna(0).mean().unstack(level='Exposure_Group')\n",
    "    AvgSumStatDict[Var].rename(index={xx: Fun_ValueLabelQuery(Var,xx) for xx in AvgSumStatDict[Var].index}, \\\n",
    "                               columns={False: 'Non-integrated', True: 'Integrated'},  inplace=True)\n",
    "    print('The average summary statistics for ', Var, ' is ')\n",
    "    print(AvgSumStatDict[Var])\n",
    "    print('\\n')\n",
    "\n",
    "pd.concat(AvgSumStatDict)[['FullSample', 'Non-integrated','Integrated']].to_excel('TableGraph/CFM_Sample_SumStat_byFinIntegration.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>period</th>\n",
       "      <th>Exposure_Group</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2014-10-01</th>\n",
       "      <th>False</th>\n",
       "      <td>4161.0</td>\n",
       "      <td>165516.237443</td>\n",
       "      <td>263846.285544</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>45800.0</td>\n",
       "      <td>216500.0</td>\n",
       "      <td>2596355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>899.0</td>\n",
       "      <td>146269.662959</td>\n",
       "      <td>262031.290234</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>27050.0</td>\n",
       "      <td>193750.0</td>\n",
       "      <td>1796947.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullSample</th>\n",
       "      <td>5060.0</td>\n",
       "      <td>162096.737352</td>\n",
       "      <td>263601.613032</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>42705.0</td>\n",
       "      <td>212678.5</td>\n",
       "      <td>2596355.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2014-11-01</th>\n",
       "      <th>False</th>\n",
       "      <td>2783.0</td>\n",
       "      <td>148752.331656</td>\n",
       "      <td>213498.477251</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4750.0</td>\n",
       "      <td>51150.0</td>\n",
       "      <td>209500.0</td>\n",
       "      <td>1239750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>578.0</td>\n",
       "      <td>132997.885813</td>\n",
       "      <td>235107.568137</td>\n",
       "      <td>50.0</td>\n",
       "      <td>5650.0</td>\n",
       "      <td>36501.0</td>\n",
       "      <td>162500.0</td>\n",
       "      <td>1710212.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2018-11-01</th>\n",
       "      <th>True</th>\n",
       "      <td>2241.0</td>\n",
       "      <td>244492.579652</td>\n",
       "      <td>326870.997838</td>\n",
       "      <td>50.0</td>\n",
       "      <td>10250.0</td>\n",
       "      <td>105545.0</td>\n",
       "      <td>369130.0</td>\n",
       "      <td>1987045.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullSample</th>\n",
       "      <td>4612.0</td>\n",
       "      <td>219729.131830</td>\n",
       "      <td>303071.354885</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8750.0</td>\n",
       "      <td>79369.0</td>\n",
       "      <td>328500.0</td>\n",
       "      <td>1987045.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2018-12-01</th>\n",
       "      <th>False</th>\n",
       "      <td>2589.0</td>\n",
       "      <td>187743.684048</td>\n",
       "      <td>252413.517426</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10050.0</td>\n",
       "      <td>93500.0</td>\n",
       "      <td>268015.0</td>\n",
       "      <td>1665000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True</th>\n",
       "      <td>2377.0</td>\n",
       "      <td>164650.599916</td>\n",
       "      <td>294778.976558</td>\n",
       "      <td>50.0</td>\n",
       "      <td>4500.0</td>\n",
       "      <td>27000.0</td>\n",
       "      <td>196800.0</td>\n",
       "      <td>2324545.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullSample</th>\n",
       "      <td>4966.0</td>\n",
       "      <td>176690.067257</td>\n",
       "      <td>273727.290936</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6250.0</td>\n",
       "      <td>51500.0</td>\n",
       "      <td>255589.0</td>\n",
       "      <td>2324545.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>153 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            count           mean            std   min  \\\n",
       "period     Exposure_Group                                               \n",
       "2014-10-01 False           4161.0  165516.237443  263846.285544   0.0   \n",
       "           True             899.0  146269.662959  262031.290234  50.0   \n",
       "           FullSample      5060.0  162096.737352  263601.613032   0.0   \n",
       "2014-11-01 False           2783.0  148752.331656  213498.477251   0.0   \n",
       "           True             578.0  132997.885813  235107.568137  50.0   \n",
       "...                           ...            ...            ...   ...   \n",
       "2018-11-01 True            2241.0  244492.579652  326870.997838  50.0   \n",
       "           FullSample      4612.0  219729.131830  303071.354885   0.0   \n",
       "2018-12-01 False           2589.0  187743.684048  252413.517426   0.0   \n",
       "           True            2377.0  164650.599916  294778.976558  50.0   \n",
       "           FullSample      4966.0  176690.067257  273727.290936   0.0   \n",
       "\n",
       "                               25%       50%       75%        max  \n",
       "period     Exposure_Group                                          \n",
       "2014-10-01 False            4500.0   45800.0  216500.0  2596355.0  \n",
       "           True             3800.0   27050.0  193750.0  1796947.0  \n",
       "           FullSample       4500.0   42705.0  212678.5  2596355.0  \n",
       "2014-11-01 False            4750.0   51150.0  209500.0  1239750.0  \n",
       "           True             5650.0   36501.0  162500.0  1710212.0  \n",
       "...                            ...       ...       ...        ...  \n",
       "2018-11-01 True            10250.0  105545.0  369130.0  1987045.0  \n",
       "           FullSample       8750.0   79369.0  328500.0  1987045.0  \n",
       "2018-12-01 False           10050.0   93500.0  268015.0  1665000.0  \n",
       "           True             4500.0   27000.0  196800.0  2324545.0  \n",
       "           FullSample       6250.0   51500.0  255589.0  2324545.0  \n",
       "\n",
       "[153 rows x 8 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Wealth_NW_1 = TempDS.groupby(['period','Exposure_Group'])['Asset_Total'].describe()\n",
    "Wealth_NW_2 = TempDS.groupby('period')['Asset_Total'].describe().reset_index()\n",
    "Wealth_NW_2['Exposure_Group'] = 'FullSample'\n",
    "Wealth_NW_2 = Wealth_NW_2.set_index(['period','Exposure_Group'])\n",
    "Wealth_NW = pd.concat([Wealth_NW_1, Wealth_NW_2]).sort_index()\n",
    "Wealth_NW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "def WeightedAvg(DS,AvgVar,WeightVar=''):\n",
    "    if WeightVar=='':\n",
    "        TempDS = DS.loc[np.isfinite(DS[AvgVar]),AvgVar]\n",
    "        return np.average(TempDS)\n",
    "    else:\n",
    "        TempDS = DS.loc[np.isfinite(DS[AvgVar]) & np.isfinite(DS[WeightVar]),[AvgVar,WeightVar]]\n",
    "        return np.average(TempDS[AvgVar],weights=TempDS[WeightVar])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "period      Exposure_Group\n",
       "2014-10-01  False             142488.322073\n",
       "            True              120712.387495\n",
       "            FullSample        138813.255764\n",
       "2014-11-01  False             128725.341050\n",
       "            True               97940.561755\n",
       "                                  ...      \n",
       "2018-11-01  True              236810.057214\n",
       "            FullSample        206589.167468\n",
       "2018-12-01  False             171834.789015\n",
       "            True              150913.273331\n",
       "            FullSample        162198.914238\n",
       "Name: 0, Length: 153, dtype: float64"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Wealth_WAvg_1 = TempDS.groupby(['period','Exposure_Group']).apply(lambda x: WeightedAvg(x,'Asset_Total','Weight'))\n",
    "Wealth_WAvg_2 = TempDS.groupby('period').apply(lambda x: WeightedAvg(x,'Asset_Total','Weight')).reset_index()\n",
    "Wealth_WAvg_2['Exposure_Group'] = 'FullSample'\n",
    "Wealth_WAvg_2 = Wealth_WAvg_2.set_index(['period','Exposure_Group'])\n",
    "Wealth_WAvg = pd.concat([Wealth_WAvg_1, Wealth_WAvg_2]).sort_index()[0]\n",
    "Wealth_WAvg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "Wealth_SumStat = Wealth_NW.join(Wealth_WAvg.rename('WAvg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "Wealth_SumStat.unstack().mean().unstack().rename(columns={False: 'Non-integrated', True: 'Integrated'}).to_excel('TableGraph/CFM_Sample_Asset_SumStat_byFinIntegration.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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